Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations89928
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 MiB
Average record size in memory160.0 B

Variable types

Text3
Numeric14
Categorical3

Alerts

Average_Rating is highly overall correlated with Deviation of star ratingsHigh correlation
Deviation of star ratings is highly overall correlated with Average_Rating and 2 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Rating is highly overall correlated with Deviation of star ratings and 1 other fieldsHigh correlation
breadth is highly overall correlated with depthHigh correlation
depth is highly overall correlated with breadthHigh correlation
sentiment_score_discrete is highly overall correlated with valenceHigh correlation
valence is highly overall correlated with Deviation of star ratings and 2 other fieldsHigh correlation
is_photo is highly imbalanced (76.6%) Imbalance
Helpfulness is highly skewed (γ1 = 102.643241) Skewed
Helpfulness has 81083 (90.2%) zeros Zeros

Reproduction

Analysis started2025-02-06 04:41:54.609548
Analysis finished2025-02-06 04:42:34.534069
Duration39.92 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:35.077169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length199
Median length166
Mean length134.77758
Min length37

Characters and Unicode

Total characters12120278
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
2nd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
3rd rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
4th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
5th rowPanasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8
ValueCountFrequency (%)
72336
 
3.7%
with 50966
 
2.6%
for 42530
 
2.2%
and 36374
 
1.9%
to 35940
 
1.8%
tv 27997
 
1.4%
wireless 25052
 
1.3%
black 22403
 
1.2%
ipad 19276
 
1.0%
mount 18331
 
0.9%
Other values (691) 1596580
82.0%
2025-02-06T13:42:35.888796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1857857
 
15.3%
e 821803
 
6.8%
o 650506
 
5.4%
a 621910
 
5.1%
t 609860
 
5.0%
i 583543
 
4.8%
r 532707
 
4.4%
n 452896
 
3.7%
l 411072
 
3.4%
s 347324
 
2.9%
Other values (70) 5230800
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12120278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1857857
 
15.3%
e 821803
 
6.8%
o 650506
 
5.4%
a 621910
 
5.1%
t 609860
 
5.0%
i 583543
 
4.8%
r 532707
 
4.4%
n 452896
 
3.7%
l 411072
 
3.4%
s 347324
 
2.9%
Other values (70) 5230800
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12120278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1857857
 
15.3%
e 821803
 
6.8%
o 650506
 
5.4%
a 621910
 
5.1%
t 609860
 
5.0%
i 583543
 
4.8%
r 532707
 
4.4%
n 452896
 
3.7%
l 411072
 
3.4%
s 347324
 
2.9%
Other values (70) 5230800
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12120278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1857857
 
15.3%
e 821803
 
6.8%
o 650506
 
5.4%
a 621910
 
5.1%
t 609860
 
5.0%
i 583543
 
4.8%
r 532707
 
4.4%
n 452896
 
3.7%
l 411072
 
3.4%
s 347324
 
2.9%
Other values (70) 5230800
43.2%

Num_of_Ratings
Real number (ℝ)

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49550.453
Minimum15398
Maximum223181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:36.067123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15398
5-th percentile16453
Q121988
median36537
Q362436
95-th percentile119789
Maximum223181
Range207783
Interquartile range (IQR)40448

Descriptive statistics

Standard deviation40059.224
Coefficient of variation (CV)0.80845324
Kurtosis5.2872358
Mean49550.453
Median Absolute Deviation (MAD)16550
Skewness2.1372403
Sum4.4559731 × 109
Variance1.6047415 × 109
MonotonicityNot monotonic
2025-02-06T13:42:36.213298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33336 1736
 
1.9%
110444 1666
 
1.9%
104579 1596
 
1.8%
76290 1586
 
1.8%
24205 1541
 
1.7%
18908 1480
 
1.6%
33087 1478
 
1.6%
119789 1436
 
1.6%
59745 1408
 
1.6%
46398 1353
 
1.5%
Other values (67) 74648
83.0%
ValueCountFrequency (%)
15398 939
1.0%
15469 968
1.1%
16023 1073
1.2%
16085 1324
1.5%
16453 1001
1.1%
17206 1287
1.4%
17230 884
1.0%
17318 985
1.1%
18061 875
1.0%
18244 1246
1.4%
ValueCountFrequency (%)
223181 1091
1.2%
201075 1283
1.4%
148591 929
1.0%
122681 1103
1.2%
119789 1436
1.6%
110468 1125
1.3%
110444 1666
1.9%
104579 1596
1.8%
100244 1346
1.5%
85201 882
1.0%

Rating
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size702.7 KiB
5
60822 
1
10924 
4
8481 
3
 
5384
2
 
4317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89928
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row3
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%

Length

2025-02-06T13:42:36.355798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:42:36.537960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%

Most occurring characters

ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 60822
67.6%
1 10924
 
12.1%
4 8481
 
9.4%
3 5384
 
6.0%
2 4317
 
4.8%
Distinct59344
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:37.166157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length100
Median length87
Mean length22.165488
Min length1

Characters and Unicode

Total characters1993298
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54324 ?
Unique (%)60.4%

Sample

1st rowIt's a good product
2nd rownothing came in?
3rd rowGreat for basement or garage use.
4th rowSurprise!
5th rowFair to good reception
ValueCountFrequency (%)
great 16234
 
4.5%
for 8898
 
2.5%
good 8664
 
2.4%
works 8357
 
2.3%
the 8243
 
2.3%
to 6878
 
1.9%
it 6822
 
1.9%
and 6104
 
1.7%
not 5768
 
1.6%
a 5627
 
1.6%
Other values (11472) 277345
77.3%
2025-02-06T13:42:38.006898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270119
 
13.6%
e 192036
 
9.6%
t 146225
 
7.3%
o 143252
 
7.2%
r 115364
 
5.8%
a 115288
 
5.8%
s 94031
 
4.7%
i 86660
 
4.3%
n 79570
 
4.0%
d 67308
 
3.4%
Other values (63) 683445
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1993298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
270119
 
13.6%
e 192036
 
9.6%
t 146225
 
7.3%
o 143252
 
7.2%
r 115364
 
5.8%
a 115288
 
5.8%
s 94031
 
4.7%
i 86660
 
4.3%
n 79570
 
4.0%
d 67308
 
3.4%
Other values (63) 683445
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1993298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
270119
 
13.6%
e 192036
 
9.6%
t 146225
 
7.3%
o 143252
 
7.2%
r 115364
 
5.8%
a 115288
 
5.8%
s 94031
 
4.7%
i 86660
 
4.3%
n 79570
 
4.0%
d 67308
 
3.4%
Other values (63) 683445
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1993298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
270119
 
13.6%
e 192036
 
9.6%
t 146225
 
7.3%
o 143252
 
7.2%
r 115364
 
5.8%
a 115288
 
5.8%
s 94031
 
4.7%
i 86660
 
4.3%
n 79570
 
4.0%
d 67308
 
3.4%
Other values (63) 683445
34.3%
Distinct89887
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Memory size702.7 KiB
2025-02-06T13:42:38.526752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6358
Median length2088
Mean length177.89664
Min length1

Characters and Unicode

Total characters15997711
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89851 ?
Unique (%)99.9%

Sample

1st rowThis radio was perfect for my father. He's older (in his 80s) and he wanted a simple transistor radio for the bathroom that runs on batteries. He didn't want anything too fancy or expensive. This fits the bill.
2nd rowI couldn't get any stations in , worthless to me. YouTube videos why I bought it buyer beware!
3rd rowThis affordable radio is perfect for my needs. Yet, I miss the quality from the higher end Sony portable. Sorry, the sound is a bit tinny. Yet, I am a fan of the controls, display and design. Good value.
4th rowSurprisingly wonderful little radio. Just what we wanted!!
5th rowVery good portable radio. Great size. Fair to good reception in a difficult reception area.I have tried more expensive radios that didpoorly.
ValueCountFrequency (%)
the 135129
 
4.5%
i 92197
 
3.1%
to 89906
 
3.0%
and 85865
 
2.9%
it 78042
 
2.6%
a 69982
 
2.3%
for 47120
 
1.6%
this 46784
 
1.6%
is 45488
 
1.5%
my 44780
 
1.5%
Other values (50701) 2260334
75.5%
2025-02-06T13:42:39.314779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2949983
18.4%
e 1459349
 
9.1%
t 1207422
 
7.5%
o 1001664
 
6.3%
a 913838
 
5.7%
s 788530
 
4.9%
i 784619
 
4.9%
n 756681
 
4.7%
r 698342
 
4.4%
h 585360
 
3.7%
Other values (68) 4851923
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15997711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2949983
18.4%
e 1459349
 
9.1%
t 1207422
 
7.5%
o 1001664
 
6.3%
a 913838
 
5.7%
s 788530
 
4.9%
i 784619
 
4.9%
n 756681
 
4.7%
r 698342
 
4.4%
h 585360
 
3.7%
Other values (68) 4851923
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15997711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2949983
18.4%
e 1459349
 
9.1%
t 1207422
 
7.5%
o 1001664
 
6.3%
a 913838
 
5.7%
s 788530
 
4.9%
i 784619
 
4.9%
n 756681
 
4.7%
r 698342
 
4.4%
h 585360
 
3.7%
Other values (68) 4851923
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15997711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2949983
18.4%
e 1459349
 
9.1%
t 1207422
 
7.5%
o 1001664
 
6.3%
a 913838
 
5.7%
s 788530
 
4.9%
i 784619
 
4.9%
n 756681
 
4.7%
r 698342
 
4.4%
h 585360
 
3.7%
Other values (68) 4851923
30.3%

Helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22167734
Minimum0
Maximum589
Zeros81083
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:39.482841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum589
Range589
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1477102
Coefficient of variation (CV)14.199513
Kurtosis15575.65
Mean0.22167734
Median Absolute Deviation (MAD)0
Skewness102.64324
Sum19935
Variance9.9080795
MonotonicityNot monotonic
2025-02-06T13:42:39.636426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81083
90.2%
1 6428
 
7.1%
2 1183
 
1.3%
3 425
 
0.5%
4 229
 
0.3%
5 146
 
0.2%
6 84
 
0.1%
7 72
 
0.1%
8 43
 
< 0.1%
9 34
 
< 0.1%
Other values (49) 201
 
0.2%
ValueCountFrequency (%)
0 81083
90.2%
1 6428
 
7.1%
2 1183
 
1.3%
3 425
 
0.5%
4 229
 
0.3%
5 146
 
0.2%
6 84
 
0.1%
7 72
 
0.1%
8 43
 
< 0.1%
9 34
 
< 0.1%
ValueCountFrequency (%)
589 1
< 0.1%
283 1
< 0.1%
243 1
< 0.1%
235 1
< 0.1%
189 1
< 0.1%
174 1
< 0.1%
136 1
< 0.1%
132 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%

is_photo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size702.7 KiB
0
86499 
1
 
3429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89928
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

Length

2025-02-06T13:42:39.787627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:42:39.920496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86499
96.2%
1 3429
 
3.8%

price
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.188978
Minimum5.99
Maximum175.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:40.051134image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.99
5-th percentile6.99
Q111.99
median19.99
Q336.99
95-th percentile87.14
Maximum175.99
Range170
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.108753
Coefficient of variation (CV)0.96299201
Kurtosis9.1687656
Mean29.188978
Median Absolute Deviation (MAD)10
Skewness2.6923714
Sum2624906.5
Variance790.102
MonotonicityNot monotonic
2025-02-06T13:42:40.208842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.99 7400
 
8.2%
13.99 5106
 
5.7%
29.99 3889
 
4.3%
11.99 3585
 
4.0%
9.99 3278
 
3.6%
15.99 3147
 
3.5%
23.99 2613
 
2.9%
37.99 2523
 
2.8%
39.99 2466
 
2.7%
8.97 2288
 
2.5%
Other values (43) 53633
59.6%
ValueCountFrequency (%)
5.99 1196
1.3%
6.36 944
1.0%
6.44 1037
1.2%
6.99 1921
2.1%
7.82 963
1.1%
7.95 1436
1.6%
7.99 1131
1.3%
8.54 1009
1.1%
8.97 2288
2.5%
8.99 1821
2.0%
ValueCountFrequency (%)
175.99 1149
1.3%
118 1324
1.5%
101 1282
1.4%
87.14 1045
1.2%
79.99 1238
1.4%
74.95 985
1.1%
59.9 1586
1.8%
57 1108
1.2%
55.96 1273
1.4%
51 1127
1.3%

time_elapsed
Real number (ℝ)

Distinct1333
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.75917
Minimum0
Maximum1332
Zeros75
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:40.396490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120
Q1474
median783
Q31022
95-th percentile1190
Maximum1332
Range1332
Interquartile range (IQR)548

Descriptive statistics

Standard deviation339.54311
Coefficient of variation (CV)0.46211482
Kurtosis-0.91723674
Mean734.75917
Median Absolute Deviation (MAD)264
Skewness-0.3783387
Sum66075423
Variance115289.52
MonotonicityNot monotonic
2025-02-06T13:42:40.571563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1157 160
 
0.2%
1177 150
 
0.2%
1167 148
 
0.2%
1171 148
 
0.2%
1159 147
 
0.2%
1164 146
 
0.2%
1183 142
 
0.2%
1166 138
 
0.2%
1151 135
 
0.2%
1175 135
 
0.2%
Other values (1323) 88479
98.4%
ValueCountFrequency (%)
0 75
0.1%
1 11
 
< 0.1%
2 11
 
< 0.1%
3 10
 
< 0.1%
4 9
 
< 0.1%
5 13
 
< 0.1%
6 23
 
< 0.1%
7 19
 
< 0.1%
8 20
 
< 0.1%
9 21
 
< 0.1%
ValueCountFrequency (%)
1332 2
 
< 0.1%
1331 2
 
< 0.1%
1330 10
< 0.1%
1329 5
< 0.1%
1328 3
 
< 0.1%
1327 6
< 0.1%
1326 11
< 0.1%
1325 12
< 0.1%
1324 12
< 0.1%
1323 8
< 0.1%

Average_Rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5057635
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:40.713611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4
Q14.4
median4.6
Q34.7
95-th percentile4.8
Maximum4.9
Range1.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.22879258
Coefficient of variation (CV)0.050777761
Kurtosis1.1717588
Mean4.5057635
Median Absolute Deviation (MAD)0.1
Skewness-1.2164853
Sum405194.3
Variance0.052346045
MonotonicityNot monotonic
2025-02-06T13:42:40.834584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.6 26157
29.1%
4.7 19901
22.1%
4.5 12235
13.6%
4.3 10590
11.8%
4.4 5712
 
6.4%
4.8 4073
 
4.5%
3.9 2791
 
3.1%
4.1 2499
 
2.8%
4.2 2398
 
2.7%
3.8 1586
 
1.8%
Other values (2) 1986
 
2.2%
ValueCountFrequency (%)
3.8 1586
 
1.8%
3.9 2791
 
3.1%
4 1001
 
1.1%
4.1 2499
 
2.8%
4.2 2398
 
2.7%
4.3 10590
11.8%
4.4 5712
 
6.4%
4.5 12235
13.6%
4.6 26157
29.1%
4.7 19901
22.1%
ValueCountFrequency (%)
4.9 985
 
1.1%
4.8 4073
 
4.5%
4.7 19901
22.1%
4.6 26157
29.1%
4.5 12235
13.6%
4.4 5712
 
6.4%
4.3 10590
11.8%
4.2 2398
 
2.7%
4.1 2499
 
2.8%
4 1001
 
1.1%

title_length
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9915154
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:40.967392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum24
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7558886
Coefficient of variation (CV)0.69043665
Kurtosis4.135236
Mean3.9915154
Median Absolute Deviation (MAD)1
Skewness1.7449753
Sum358949
Variance7.5949217
MonotonicityNot monotonic
2025-02-06T13:42:41.112736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 24423
27.2%
3 15509
17.2%
4 11967
13.3%
1 9237
 
10.3%
5 9050
 
10.1%
6 6462
 
7.2%
7 4171
 
4.6%
8 2890
 
3.2%
9 1861
 
2.1%
10 1292
 
1.4%
Other values (14) 3066
 
3.4%
ValueCountFrequency (%)
1 9237
 
10.3%
2 24423
27.2%
3 15509
17.2%
4 11967
13.3%
5 9050
 
10.1%
6 6462
 
7.2%
7 4171
 
4.6%
8 2890
 
3.2%
9 1861
 
2.1%
10 1292
 
1.4%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 2
 
< 0.1%
22 7
 
< 0.1%
21 14
 
< 0.1%
20 23
 
< 0.1%
19 41
 
< 0.1%
18 91
 
0.1%
17 116
0.1%
16 183
0.2%
15 267
0.3%

text_length
Real number (ℝ)

Distinct457
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.320812
Minimum1
Maximum1066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:41.266031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median20
Q342
95-th percentile104
Maximum1066
Range1065
Interquartile range (IQR)33

Descriptive statistics

Standard deviation40.959623
Coefficient of variation (CV)1.2292504
Kurtosis43.085438
Mean33.320812
Median Absolute Deviation (MAD)13
Skewness4.5172536
Sum2996474
Variance1677.6907
MonotonicityNot monotonic
2025-02-06T13:42:41.424495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3384
 
3.8%
6 3293
 
3.7%
7 3074
 
3.4%
4 3014
 
3.4%
8 2753
 
3.1%
9 2652
 
2.9%
10 2573
 
2.9%
11 2486
 
2.8%
12 2302
 
2.6%
13 2214
 
2.5%
Other values (447) 62183
69.1%
ValueCountFrequency (%)
1 539
 
0.6%
2 1890
2.1%
3 2143
2.4%
4 3014
3.4%
5 3384
3.8%
6 3293
3.7%
7 3074
3.4%
8 2753
3.1%
9 2652
2.9%
10 2573
2.9%
ValueCountFrequency (%)
1066 1
< 0.1%
1063 1
< 0.1%
884 1
< 0.1%
814 1
< 0.1%
778 1
< 0.1%
777 1
< 0.1%
768 1
< 0.1%
763 1
< 0.1%
692 1
< 0.1%
684 1
< 0.1%

Deviation of star ratings
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97696046
Minimum0
Maximum3.9
Zeros117
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:41.585619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.5
Q31
95-th percentile3.6
Maximum3.9
Range3.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.037553
Coefficient of variation (CV)1.0620215
Kurtosis1.1941084
Mean0.97696046
Median Absolute Deviation (MAD)0.2
Skewness1.631152
Sum87856.1
Variance1.0765162
MonotonicityNot monotonic
2025-02-06T13:42:41.732198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.4 19400
21.6%
0.3 16288
18.1%
0.5 9327
10.4%
0.7 7743
 
8.6%
0.6 6085
 
6.8%
0.2 3627
 
4.0%
3.6 2556
 
2.8%
0.8 1728
 
1.9%
3.3 1718
 
1.9%
0.9 1672
 
1.9%
Other values (30) 19784
22.0%
ValueCountFrequency (%)
0 117
 
0.1%
0.1 1317
 
1.5%
0.2 3627
 
4.0%
0.3 16288
18.1%
0.4 19400
21.6%
0.5 9327
10.4%
0.6 6085
 
6.8%
0.7 7743
 
8.6%
0.8 1728
 
1.9%
0.9 1672
 
1.9%
ValueCountFrequency (%)
3.9 17
 
< 0.1%
3.8 348
 
0.4%
3.7 1669
1.9%
3.6 2556
2.8%
3.5 1233
1.4%
3.4 824
 
0.9%
3.3 1718
1.9%
3.2 351
 
0.4%
3.1 546
 
0.6%
3 158
 
0.2%

FOG Index
Real number (ℝ)

High correlation 

Distinct1587
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1005185
Minimum0
Maximum68.71
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:41.898396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q14.4
median6.82
Q39.06
95-th percentile13.95
Maximum68.71
Range68.71
Interquartile range (IQR)4.66

Descriptive statistics

Standard deviation4.2071998
Coefficient of variation (CV)0.59252008
Kurtosis11.369965
Mean7.1005185
Median Absolute Deviation (MAD)2.3
Skewness2.0016094
Sum638535.43
Variance17.70053
MonotonicityNot monotonic
2025-02-06T13:42:42.068066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2635
 
2.9%
2.4 2395
 
2.7%
1.6 2272
 
2.5%
2.8 2060
 
2.3%
8.04 1745
 
1.9%
3.2 1741
 
1.9%
1.2 1629
 
1.8%
10 1530
 
1.7%
3.6 1481
 
1.6%
0.8 1428
 
1.6%
Other values (1577) 71012
79.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.4 416
 
0.5%
0.8 1428
1.6%
1 5
 
< 0.1%
1.2 1629
1.8%
1.32 21
 
< 0.1%
1.36 1
 
< 0.1%
1.4 170
 
0.2%
1.44 1
 
< 0.1%
1.48 18
 
< 0.1%
ValueCountFrequency (%)
68.71 1
< 0.1%
56.55 1
< 0.1%
56.07 1
< 0.1%
54.79 1
< 0.1%
50.77 1
< 0.1%
49.46 1
< 0.1%
48.47 1
< 0.1%
47.66 1
< 0.1%
46.62 1
< 0.1%
44.7 1
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct1855
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.775016
Minimum-386.39
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative562
Negative (%)0.6%
Memory size702.7 KiB
2025-02-06T13:42:42.242567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-386.39
5-th percentile50.5
Q173.34
median83.25
Q392.8
95-th percentile113.1
Maximum206.84
Range593.23
Interquartile range (IQR)19.46

Descriptive statistics

Standard deviation19.915517
Coefficient of variation (CV)0.24354036
Kurtosis16.810004
Mean81.775016
Median Absolute Deviation (MAD)9.57
Skewness-2.059701
Sum7353863.6
Variance396.62781
MonotonicityNot monotonic
2025-02-06T13:42:42.389530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.24 1083
 
1.2%
90.77 1077
 
1.2%
88.74 1008
 
1.1%
84.68 995
 
1.1%
99.23 991
 
1.1%
83.32 988
 
1.1%
92.8 985
 
1.1%
89.75 934
 
1.0%
87.72 861
 
1.0%
75.88 857
 
1.0%
Other values (1845) 80149
89.1%
ValueCountFrequency (%)
-386.39 1
 
< 0.1%
-301.79 1
 
< 0.1%
-217.19 2
 
< 0.1%
-175.9 1
 
< 0.1%
-133.6 2
 
< 0.1%
-132.59 26
< 0.1%
-109.24 1
 
< 0.1%
-93.33 1
 
< 0.1%
-91.3 12
< 0.1%
-75.4 2
 
< 0.1%
ValueCountFrequency (%)
206.84 3
 
< 0.1%
121.22 271
 
0.3%
120.21 734
0.8%
119.19 669
0.7%
118.89 1
 
< 0.1%
118.68 30
 
< 0.1%
118.18 717
0.8%
117.97 1
 
< 0.1%
117.87 3
 
< 0.1%
117.67 43
 
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct81423
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45476814
Minimum9.29 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:43.082989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum9.29 × 10-18
5-th percentile0.065279989
Q10.32324519
median0.47160911
Q30.59594569
95-th percentile0.74108588
Maximum1
Range1
Interquartile range (IQR)0.2727005

Descriptive statistics

Standard deviation0.20011389
Coefficient of variation (CV)0.44003499
Kurtosis-0.015519818
Mean0.45476814
Median Absolute Deviation (MAD)0.13421368
Skewness-0.23433381
Sum40896.39
Variance0.040045571
MonotonicityNot monotonic
2025-02-06T13:42:43.256341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1138
 
1.3%
1.09 × 10-17162
 
0.2%
9.9 × 10-18138
 
0.2%
1.97 × 10-17119
 
0.1%
9.91 × 10-18104
 
0.1%
0.274296293 100
 
0.1%
0.025064165 66
 
0.1%
0.473495025 59
 
0.1%
9.29 × 10-1854
 
0.1%
1.14 × 10-1754
 
0.1%
Other values (81413) 87934
97.8%
ValueCountFrequency (%)
9.29 × 10-1854
 
0.1%
9.73 × 10-1833
 
< 0.1%
9.9 × 10-18138
0.2%
9.91 × 10-18104
0.1%
1.02 × 10-1729
 
< 0.1%
1.08 × 10-1722
 
< 0.1%
1.09 × 10-17162
0.2%
1.12 × 10-1750
 
0.1%
1.13 × 10-171
 
< 0.1%
1.14 × 10-1754
 
0.1%
ValueCountFrequency (%)
1 1138
1.3%
0.940235273 1
 
< 0.1%
0.926402675 1
 
< 0.1%
0.920337669 1
 
< 0.1%
0.919140793 1
 
< 0.1%
0.917420184 1
 
< 0.1%
0.915049416 1
 
< 0.1%
0.912612395 1
 
< 0.1%
0.911393572 1
 
< 0.1%
0.910577563 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct81998
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6904579
Minimum0.049089481
Maximum1.5871279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:43.423451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.049089481
5-th percentile0.33907086
Q10.50773113
median0.65637401
Q30.84881641
95-th percentile1.1634078
Maximum1.5871279
Range1.5380384
Interquartile range (IQR)0.34108528

Descriptive statistics

Standard deviation0.25691204
Coefficient of variation (CV)0.37208936
Kurtosis0.34865904
Mean0.6904579
Median Absolute Deviation (MAD)0.16636472
Skewness0.50102025
Sum62091.498
Variance0.066003796
MonotonicityNot monotonic
2025-02-06T13:42:43.586744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.049089481 1138
 
1.3%
1.212548325 163
 
0.2%
1.318837272 138
 
0.2%
1.495871608 113
 
0.1%
1.521700354 106
 
0.1%
1.042203039 97
 
0.1%
1.428659645 70
 
0.1%
0.731167094 65
 
0.1%
1.300849521 60
 
0.1%
1.538962761 60
 
0.1%
Other values (81988) 87918
97.8%
ValueCountFrequency (%)
0.049089481 1138
1.3%
0.110402694 1
 
< 0.1%
0.123922565 1
 
< 0.1%
0.126351175 1
 
< 0.1%
0.134566919 1
 
< 0.1%
0.134856935 1
 
< 0.1%
0.136756858 1
 
< 0.1%
0.140297912 1
 
< 0.1%
0.142726955 1
 
< 0.1%
0.143530826 1
 
< 0.1%
ValueCountFrequency (%)
1.587127912 24
< 0.1%
1.586637299 1
 
< 0.1%
1.586620458 1
 
< 0.1%
1.582771839 2
 
< 0.1%
1.577139402 7
 
< 0.1%
1.575777877 1
 
< 0.1%
1.574594181 1
 
< 0.1%
1.55991293 1
 
< 0.1%
1.556294965 1
 
< 0.1%
1.548250345 1
 
< 0.1%

valence
Real number (ℝ)

High correlation 

Distinct3980
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7721981
Minimum1.011
Maximum4.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size702.7 KiB
2025-02-06T13:42:43.762315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.011
5-th percentile1.366
Q12.98375
median4.308
Q34.707
95-th percentile4.9
Maximum4.991
Range3.98
Interquartile range (IQR)1.72325

Descriptive statistics

Standard deviation1.1883453
Coefficient of variation (CV)0.31502728
Kurtosis-0.49242643
Mean3.7721981
Median Absolute Deviation (MAD)0.501
Skewness-0.96626579
Sum339226.23
Variance1.4121645
MonotonicityNot monotonic
2025-02-06T13:42:43.935245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.868 132
 
0.1%
4.83 125
 
0.1%
4.816 123
 
0.1%
4.792 120
 
0.1%
4.832 118
 
0.1%
4.782 116
 
0.1%
4.84 114
 
0.1%
4.82 113
 
0.1%
4.808 113
 
0.1%
4.831 113
 
0.1%
Other values (3970) 88741
98.7%
ValueCountFrequency (%)
1.011 1
 
< 0.1%
1.013 2
 
< 0.1%
1.014 1
 
< 0.1%
1.015 3
 
< 0.1%
1.016 1
 
< 0.1%
1.017 8
< 0.1%
1.018 1
 
< 0.1%
1.019 3
 
< 0.1%
1.02 1
 
< 0.1%
1.021 2
 
< 0.1%
ValueCountFrequency (%)
4.991 1
 
< 0.1%
4.99 2
< 0.1%
4.989 2
< 0.1%
4.988 4
< 0.1%
4.987 1
 
< 0.1%
4.986 3
< 0.1%
4.985 3
< 0.1%
4.984 1
 
< 0.1%
4.983 1
 
< 0.1%
4.982 4
< 0.1%

sentiment_score_discrete
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size702.7 KiB
5
46046 
4
17528 
1
10793 
2
8016 
3
7545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89928
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row4
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

Length

2025-02-06T13:42:44.093005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T13:42:44.240794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

Most occurring characters

ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 46046
51.2%
4 17528
 
19.5%
1 10793
 
12.0%
2 8016
 
8.9%
3 7545
 
8.4%

arousal
Real number (ℝ)

Distinct89841
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.055945352
Minimum-0.58566369
Maximum0.62138115
Zeros0
Zeros (%)0.0%
Negative42488
Negative (%)47.2%
Memory size702.7 KiB
2025-02-06T13:42:44.397339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-0.58566369
5-th percentile-0.53633681
Q1-0.21464333
median0.015992218
Q30.11185267
95-th percentile0.26572369
Maximum0.62138115
Range1.2070448
Interquartile range (IQR)0.326496

Descriptive statistics

Standard deviation0.23997763
Coefficient of variation (CV)-4.2895009
Kurtosis-0.31090108
Mean-0.055945352
Median Absolute Deviation (MAD)0.12193916
Skewness-0.52732977
Sum-5030.9976
Variance0.057589265
MonotonicityNot monotonic
2025-02-06T13:42:44.572093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.388847079 4
 
< 0.1%
0.144103437 4
 
< 0.1%
-0.578376945 4
 
< 0.1%
0.143925741 3
 
< 0.1%
0.128265862 3
 
< 0.1%
0.103131643 2
 
< 0.1%
-0.13030467 2
 
< 0.1%
0.15023217 2
 
< 0.1%
0.042868702 2
 
< 0.1%
-0.017180231 2
 
< 0.1%
Other values (89831) 89899
> 99.9%
ValueCountFrequency (%)
-0.585663687 1
< 0.1%
-0.585325381 1
< 0.1%
-0.584975552 1
< 0.1%
-0.584104509 1
< 0.1%
-0.584058099 1
< 0.1%
-0.583990488 1
< 0.1%
-0.583907921 1
< 0.1%
-0.583852327 1
< 0.1%
-0.583806007 1
< 0.1%
-0.583746242 1
< 0.1%
ValueCountFrequency (%)
0.62138115 1
< 0.1%
0.620142733 1
< 0.1%
0.616403095 1
< 0.1%
0.614642566 1
< 0.1%
0.612602407 1
< 0.1%
0.610246954 1
< 0.1%
0.609922073 1
< 0.1%
0.60943808 1
< 0.1%
0.609031218 1
< 0.1%
0.608121139 1
< 0.1%

Interactions

2025-02-06T13:42:31.217970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.029365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.033363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.094043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.174824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.240218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.231437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.266147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.932508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.117111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.249576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.339965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.382771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.535292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.365317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.175587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.164721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.237186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.302618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.366667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.369881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.402907image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.070911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.254240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.384451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.478876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.532045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.669153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.514004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.311247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.306575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.386404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.456580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.510021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.514287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.551090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.240096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.401857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.535899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.622997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.682889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.820769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.673697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.449818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.461882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.535883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.610403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.651355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.661797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.702270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.400423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.555729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.683643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.766297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.846299image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.971903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.820130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.582544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.609617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.686124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.747941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.796916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.800576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.839776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.555449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.694279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.830313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.920221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.998384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.120976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.971401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.710995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.760927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.828476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.883946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.933052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.937655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:16.483514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.707106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.832803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.974965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.061916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.148725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.263583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.112819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.846491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:05.904263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:07.971107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.024776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.064743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.068351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:16.636763image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:18.864668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:20.971063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.130112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.199384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.290431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.411255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.244988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:03.977402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.043525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.108319image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.156239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.200326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.220046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:16.768539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.018860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.126291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.265884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.331259image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.433200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.567502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.422639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.142680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.210731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.261999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.317388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.356349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.389271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:16.927819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.184584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.295444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.434699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.492362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.613403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.732161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.569822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.280811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.356970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.419079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.465326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.504627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.543252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.080020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.350628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.452589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.582622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.641078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.765576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:29.889802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.721792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.425007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.498491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.555517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.625326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.643471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.684518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.228323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.501558image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.615419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.719285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.790658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:27.926157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:30.044669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:32.863961image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.559345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.638346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.699944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.766883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.783573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.828037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.428790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.654510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.771449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:23.867829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:25.924747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.074671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:30.733386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:33.028029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.718059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.797353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:08.861341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:10.920160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:12.935565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:14.975102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.628834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.817702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:21.936178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.033306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.078450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.235384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:30.896567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:33.187673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:04.891230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:06.953058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:09.018578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:11.092773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:13.082311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:15.122731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:17.782026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:19.973388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:22.098030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:24.185489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:26.236443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:28.391158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-02-06T13:42:31.053868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-02-06T13:42:44.738071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Average_RatingDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessNum_of_RatingsRatingarousalbreadthdepthis_photopricesentiment_score_discretetext_lengthtime_elapsedtitle_lengthvalence
Average_Rating1.000-0.551-0.0260.015-0.0260.2150.1200.0190.069-0.0330.081-0.0050.107-0.0830.075-0.0560.171
Deviation of star ratings-0.5511.0000.037-0.0370.146-0.1350.811-0.093-0.115-0.0210.0470.0050.4520.173-0.0560.117-0.547
FOG Index-0.0260.0371.000-0.7320.082-0.0290.037-0.019-0.2050.1550.0350.0070.0480.354-0.0300.140-0.022
Flesch Reading Ease0.015-0.037-0.7321.000-0.0650.0210.0370.0250.196-0.1580.039-0.0240.042-0.1680.020-0.0890.016
Helpfulness-0.0260.1460.082-0.0651.000-0.0180.003-0.006-0.1410.0660.0310.0490.0030.241-0.0880.101-0.173
Num_of_Ratings0.215-0.135-0.0290.021-0.0181.0000.050-0.0620.0450.0330.031-0.0650.046-0.092-0.150-0.0470.005
Rating0.1200.8110.0370.0370.0030.0501.0000.1060.0740.0420.0180.0740.4910.0570.0340.0790.503
arousal0.019-0.093-0.0190.025-0.006-0.0620.1061.0000.0480.0540.0280.0840.1360.0780.055-0.0310.296
breadth0.069-0.115-0.2050.196-0.1410.0450.0740.0481.000-0.6040.069-0.0770.089-0.4590.027-0.1530.158
depth-0.033-0.0210.155-0.1580.0660.0330.0420.054-0.6041.0000.0600.0620.0500.301-0.0060.0780.078
is_photo0.0810.0470.0350.0390.0310.0310.0180.0280.0690.0601.0000.0760.0190.0950.0320.0500.018
price-0.0050.0050.007-0.0240.049-0.0650.0740.084-0.0770.0620.0761.0000.0670.120-0.0110.047-0.002
sentiment_score_discrete0.1070.4520.0480.0420.0030.0460.4910.1360.0890.0500.0190.0671.0000.0770.0350.0820.811
text_length-0.0830.1730.354-0.1680.241-0.0920.0570.078-0.4590.3010.0950.1200.0771.000-0.0330.323-0.192
time_elapsed0.075-0.056-0.0300.020-0.088-0.1500.0340.0550.027-0.0060.032-0.0110.035-0.0331.000-0.0400.076
title_length-0.0560.1170.140-0.0890.101-0.0470.079-0.031-0.1530.0780.0500.0470.0820.323-0.0401.000-0.144
valence0.171-0.547-0.0220.016-0.1730.0050.5030.2960.1580.0780.018-0.0020.811-0.1920.076-0.1441.000

Missing values

2025-02-06T13:42:33.418727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-06T13:42:33.876606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-06T13:42:34.309198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_nameNum_of_RatingsRatingreview_titleReview_TextHelpfulnessis_photopricetime_elapsedAverage_Ratingtitle_lengthtext_lengthDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
0Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185It's a good productThis radio was perfect for my father. He's older (in his 80s) and he wanted a simple transistor radio for the bathroom that runs on batteries. He didn't want anything too fancy or expensive. This fits the bill.0034.953484.64380.46.9678.750.7327970.5895974.7535-0.364289
1Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276181nothing came in?I couldn't get any stations in , worthless to me. YouTube videos why I bought it buyer beware!0034.952184.63183.63.4088.230.5654730.6726351.1031-0.078554
2Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276183Great for basement or garage use.This affordable radio is perfect for my needs. Yet, I miss the quality from the higher end Sony portable. Sorry, the sound is a bit tinny. Yet, I am a fan of the controls, display and design. Good value.1034.956274.66391.67.0078.450.5581900.6880024.00240.099952
3Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185Surprise!Surprisingly wonderful little radio. Just what we wanted!!0034.9510894.6180.46.6033.580.4152840.7626914.91750.378918
4Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276184Fair to good receptionVery good portable radio. Great size. Fair to good reception in a difficult reception area.I have tried more expensive radios that didpoorly.0034.9510464.64220.612.0164.070.3759870.7331974.36540.109594
5Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276184Cute LITTLE thing. For local stations only.Works great for background music and local news in home office.Pros-AC (electric cord) or 4 AA batteries.-VERY portableits small. See photos.-Very simple to use.-Easy to read numbers.-Throwback classic radio look.-Sound from the one speaker is clear if the station comes in.Cons-Pulls in local stations (very local) but not half as many as your car radio (those are built to a very robust standard). If your stations are 25 miles away, you probably wont get them unless they have a powerful transmitter or youre in a very good spot for reception.-Just basic mono sound but you shouldnt be buying this for sound quality anyway.-You might expect something bigger for 290134.957524.671090.65.7978.350.7178460.3259523.9734-0.467234
6Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185good purchase!I really like this radio!0034.954794.6250.42.0066.400.2905761.0430214.74350.068621
7Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185Exactly as picturedDoesn't use much power, perfect for camping, brings in lots of stations.0034.957004.63120.44.8076.220.7127520.9253243.7544-0.416234
8Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185Great Portable RadioGreat portable radio! The radio also takes batteries when you need to use without plug. Sound quality does not change between plugin battery. It was perfect for my needs!Highly recommend!0034.959464.63300.48.3363.860.7909330.4827634.92150.094572
9Panasonic Portable AM / FM Radio, Battery Operated Analog Radio, AC Powered, Silver (RF-2400D) 22.8 x 7.8 x 10.8276185Finally happy!For a couple of years I have tried 3 "transistor" radios to replace a 30 year-old "Beach Boy" vintage style 'transistor'. I listened to that radio every day, as I worked, and was priceless for during storms. A friend had given it to me, so that make it extra sad when it finally kicked off. Soooo I went on a search. Oh my gosh. All three newer radios were junk! (one being a Panasonic). The batteries would not even last a day. Very disappointed!!All I can say is I received it on the 9th of June, and am putting it to a full test. It has been off only very seldom, for maybe a day or short periods since. I even listen as I fall asleep, and wake up to the news in the morning. This ENTIRE TIME has been powered by 4 double AA's. Two (maybe 3) days ago, it was starting to act up. I thought it was in the process of dying, and for a moment I was ticked...again thinking another piece of junk. It wasn't dead, but very erratic. It finally dawned on me to try new batteries )))) Duh. Perfect. So, then, bottom line is I am VERY happy. Both with the sound, even on high volume, and my worst fear of it eating batteries has proven just the opposite. Phenomenal. Happy dance.0034.9510124.622280.46.3584.780.4524640.4266891.9521-0.040188
product_nameNum_of_RatingsRatingreview_titleReview_TextHelpfulnessis_photopricetime_elapsedAverage_Ratingtitle_lengthtext_lengthDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadthvalencesentiment_score_discretearousal
89918Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305RuggedVery nice tablet cover, made well and easy to slip your tablet into, would buy again0019.9910554.61160.46.4097.540.5333220.6658914.65950.084003
89919Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Love itiPad air0019.992354.6220.40.80120.210.4136071.3375503.8365-0.575528
89920Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305BeautifulI am pleased with this iPad cover. It is bulkier than I wanted, but I am overall pleased.0019.998814.61180.43.6096.180.6467550.9398794.25440.362800
89921Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Love!Love this cover. Great quality love the pocket too!0019.99104.6190.46.2483.830.3260331.0748464.91650.106087
89922Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434304I love it!Cover for my iPad. Love the color0019.995804.6370.61.4093.300.0979231.1347014.67950.052057
89923Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Great iPad cover at an economical price!Its very sturdy protective! Easy to position for free standing.0019.994354.67100.410.0066.400.2192300.9487394.72250.005940
89924Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Best iPad caseNothing to dislike. Excellent quality, easy to use and good price.0019.992904.63110.49.4774.350.5606230.6474634.89050.102818
89925Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Durable cover for iPad!Love this product. Color is pretty.Easy to hold and prop up.Uses reading books, playing games, Searching internet.0019.997634.64170.44.0767.110.5118360.7006174.87050.084424
89926Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Worth itQuick shipping. Fits ipad easily. Nice colors.0019.998044.6270.48.5164.370.6843321.2010024.63150.056716
89927Fintie Case for iPad 9.7 2018 2017 / iPad Air 2 / iPad Air 1 - [Corner Protection] Multi-Angle Viewing Folio Cover w/Pocket, Auto Wake/Sleep for iPad 6th / 5th Generation, Ocean Marble434305Great!Sturdy, easy to hold and maneuver, durable0019.9910124.6170.48.5181.290.2793870.9171984.6795-0.293943